Data-driven generalized minimum variance regulatory control with unknown disturbance

摘要

This paper provides a new generalized minimum variance (GMV) strategy based on input-output measurements. The proposed method can perform with no need for plant characteristics nor disturbance ones. The paper shows that the optimization of the proposed criterion results in the control parameters which achieve the GMV control. The application to datasets obtained from a continuous stirred tank reactor (CSTR) demonstrates the efficiency of the proposed method.